(A note by Martin Porter.)
The Schinke Latin stemming algorithm is described in,
Schinke R, Greengrass M, Robertson AM and Willett P (1996) A stemming algorithm for Latin text databases. Journal of Documentation, 52: 172-187.
It has the feature that it stems each word to two forms, noun and verb. For example,
NOUN VERB ---- ---- aquila aquil aquila portat portat porta portis port por
Here (slightly reformatted) are the rules of the stemmer,
1. (start) 2. Convert all occurrences of the letters 'j' or 'v' to 'i' or 'u', respectively. 3. If the word ends in '-que' then if the word is on the list shown in Figure 4, then write the original word to both the noun-based and verb-based stem dictionaries and go to 8. else remove '-que' [Figure 4 was atque quoque neque itaque absque apsque abusque adaeque adusque denique deque susque oblique peraeque plenisque quandoque quisque quaeque cuiusque cuique quemque quamque quaque quique quorumque quarumque quibusque quosque quasque quotusquisque quousque ubique undique usque uterque utique utroque utribique torque coque concoque contorque detorque decoque excoque extorque obtorque optorque retorque recoque attorque incoque intorque praetorque] 4. Match the end of the word against the suffix list show in Figure 6(a), removing the longest matching suffix, (if any). [Figure 6(a) was -ibus -ius -ae -am -as -em -es -ia -is -nt -os -ud -um -us -a -e -i -o -u] 5. If the resulting stem contains at least two characters then write this stem to the noun-based stem dictionary. 6. Match the end of the word against the suffix list show in Figure 6(b), identifying the longest matching suffix, (if any). [Figure 6(b) was -iuntur-beris -erunt -untur -iunt -mini -ntur -stis -bor -ero -mur -mus -ris -sti -tis -tur -unt -bo -ns -nt -ri -m -r -s -t] If any of the following suffixes are found then convert them as shown: '-iuntur', '-erunt', '-untur', '-iunt', and '-unt', to '-i'; '-beris', '-bor', and '-bo' to '-bi'; '-ero' to '-eri' else remove the suffix in the normal way. 7. If the resulting stem contains at least two characters then write this stem to the verb-based stem dictionary. 8. (end)
Unfortunately I was not able to make the rules match the examples given, which led to the following email correspondence,
From: Martin Porter To: Peter Willett Date: Mon Sep 10 15:11:51 2001 Subject: Re: Stemming algorithms > ... I'm no longer working in the IR area, >spending all of my time on computational chemistry/drug discovery >research but I guess that Mark Sanderson would be interested in >Snowball - do you mind if I pass your email onto him? Peter, Well, actually, I do have a question, if you can cast your mind back. I've implemented the Latin Stemmer in Snowball (see below: you'll have to guess the semantics, but I'm sure you'll agree the syntax looks nice), and find that Fig 5 of the 1996 Schinke paper doesn't correspond to the algorithm of fig 7, but to the algorithm with the extra rules concerning -ba-, -bi-, -sse- mentioned on page 182. Which is the "correct" algorithm - with or without those rules? If with, what is the exact criterion for their removal? A bigger problem is why the -nt is not removed from 'Apparebunt', given -nt as an ending in 6(a). Is -nt a misprint? Sorry to bother you with this, but the paper says you are the one "to whom all correspondence should be addressed" :-) Martin Here is your algorithm in Snowball. The generated code will do about 1 million Latin word in 5 seconds: -------strings ( noun_form verb_form ) routines ( map_letters que_word ) externals ( stem ) define map_letters as ( do repeat ( goto ( ['j'] ) <- 'i' ) do repeat ( goto ( ['v'] ) <- 'u' ) ) backwardmode ( define que_word as ( ['que'] ( among ( 'at' 'quo' 'ne' 'ita' 'abs' 'aps' 'abus' 'adae' 'adus' 'deni' 'de' 'sus' 'obli' 'perae' 'plenis' 'quando' 'quis' 'quae' 'cuius' 'cui' 'quem' 'quam' 'qua' 'qui' 'quorum' 'quarum' 'quibus' 'quos' 'quas' 'quotusquis' 'quous' 'ubi' 'undi' 'us' 'uter' 'uti' 'utro' 'utribi' 'tor' 'co' 'conco' 'contor' 'detor' 'deco' 'exco' 'extor' 'obtor' 'optor' 'retor' 'reco' 'attor' 'inco' 'intor' 'praetor' ) atlimit ] => noun_form => verb_form ) or fail(delete) ) ) define stem as ( map_letters backwards ( que_word or ( => noun_form => verb_form $noun_form backwards try ( [substring] hop 2 among ( 'ibus' 'ius' 'ae' 'am' 'as' 'em' 'es' 'ia' 'is' 'nt' 'os' 'ud' 'um' 'us' 'a' 'e' 'i' 'o' 'u' (delete) ) ) $verb_form backwards try ( [substring] hop 2 among ( 'iuntur' 'erunt' 'untur' 'iunt' 'unt' (<-'i') 'beris' 'bor' 'bo' (<-'bi') 'ero' (<-'eri') 'mini' 'ntur' 'stis' 'mur' 'mus' 'ris' 'sti' 'tis' 'tur' 'ns' 'nt' 'ri' 'm' 'r' 's' 't' (delete) ) ) ) ) /* the stemmed words are left in noun-form and verb-form, and can be picked up as C strings at z->S and z->S through the API. */ )
From: Peter Willett To: Martin Porter Date: Mon Sep 10 20:25:24 2001 Subject: Re: Stemming algorithms Martin Sorry - I just cannot answer. Robertson has retired to Dorset while Schinke is now in - I think - Canada Peter
Following this, I was unable to contact Schinke, and so the problems have remained unresolved.
The linked zip file contains the stemmer,
generated C version, and sample data.
(The stemmer differs slightly from the version in the email above in that
it assembles the noun- and verb-forms of the stem in a single string with
voc.txt is a sample vocabulary, and
joined.txt the vocabulary
joined with the two stemmed forms as three column output.